class_docstr: A CQL-SAC Agent based on the SAC Agent.
docstr: Flattens the batch and tile dimensions into a single dimension.
  
  Args:
    original_tensor: Input tensor of shape [batch_size, tile, dim].
  
  Returns:
    Flattened tensor with the outer dimension (batch_size * tile).
docstr_tok: ["Flattens","the","batch","and","tile","dimensions","into","a","single","dimension",".","Args",":","original_tensor",":","Input","tensor","of","shape","[","batch_size",",","tile",",","dim","]",".","Returns",":","Flattened","tensor","with","the","outer","dimension","(","batch_size","*","tile",")","."]
code: def _flattened_multibatch_tensor(
      self, original_tensor: types.Tensor
  ) -> types.Tensor:
      """Flattens the batch and tile dimensions into a single dimension.
  
      Args:
        original_tensor: Input tensor of shape [batch_size, tile, dim].
  
      Returns:
        Flattened tensor with the outer dimension (batch_size * tile).
      """
      spec = tf.TensorSpec(
          shape=original_tensor.shape[2:], dtype=original_tensor.dtype
      )
      (
          flattened_tensor,
          _,
      ) = nest_utils.flatten_multi_batched_nested_tensors(
          original_tensor, spec
      )
      return flattened_tensor
  
imports: from typing import Callable, Dict, NamedTuple, Optional, Text, Tuple, Union
  import gin
  import tensorflow
  import tensorflow_probability
  from tf_agents.agents import tf_agent
  from tf_agents.agents.sac import sac_agent
  from tf_agents.networks import network
  from tf_agents.policies import actor_policy
  from tf_agents.policies import tf_policy
  from tf_agents.trajectories import time_step
  from tf_agents.typing import types
  from tf_agents.utils import common
  from tf_agents.utils import nest_utils
  from tf_agents.utils import object_identity
label_desc: Use image node_id 7 for calling the CqlSacAgent obj's underlying member method code with example usage: obj._flattened_multibatch_tensor(original_tensor) and returns: flattened_tensor